A Scoping Review of Obesity Educational Interventions in Undergraduate and Postgraduate Medical Education
Bibliographic record
Abstract
BACKGROUND: A significant proportion of doctors feel they are not qualified or successful at treating obesity. Poor readiness to treat obesity may be due to the inconsistent quality of obesity education in Canadian medical schools. These inconsistencies highlight a need for evidence-based interventions that teach health professionals how to be confident in managing the various health and social complexities that accompany obesity. The objectives of this literature review were to (1) determine what educational interventions have been used to improve obesity training in medical education; (2) evaluate the best practices on designing obesity educational interventions; and (3) analyze whether the literature has any guidance on teaching obesity in a nonstigmatizing manner. METHODS: We conducted searches in MEDLINE (Ovid), Embase (Ovid), ERIC (EBSCOhost), CINAHL (EBSCOhost), and Web of Science (Clarivate) for obesity-related health education interventions across various health disciplines. Our search strategy produced 30 full-text articles. RESULTS: The included articles revealed a significant variance in educational interventions' goals, duration, and delivery methods. Undergraduate obesity education focuses more on knowledge of obesity, whereas postgraduate education focuses more on patient care. There is some evidence that interactive learning interventions have greater success in influencing student attitudes and practice behaviors towards obesity. While some interventions were able to positively affect explicit antifat stigma, only one of them had a positive effect on implicit biases. CONCLUSION: More research needs to be done to determine effective strategies for teaching obesity education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".